"""simple docstring"""


from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   Optional[int]:




						'''simple docstring'''



						return ConvertCommand(
						    args.model_type  ,       args.tf_checkpoint  ,       args.pytorch_dump_output  ,       args.config  ,       args.finetuning_task_name							)


a				='\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n'






class   __UpperCAmelCase		(      __lowerCAmelCase      ):

				@staticmethod
				def 						_a      ( _lowerCamelCase ):
										lowerCamelCase__   =parser.add_parser(
										    "convert"      ,					help="CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints."      ,					)
										train_parser.add_argument("--model_type"      ,					type=_lowerCamelCase      ,					required=_lowerCamelCase      ,					help="Model's type." )
										train_parser.add_argument(
										    "--tf_checkpoint"      ,					type=_lowerCamelCase      ,					required=_lowerCamelCase      ,					help="TensorFlow checkpoint path or folder." )
										train_parser.add_argument(
										    "--pytorch_dump_output"      ,					type=_lowerCamelCase      ,					required=_lowerCamelCase      ,					help="Path to the PyTorch saved model output." )
										train_parser.add_argument("--config"      ,					type=_lowerCamelCase      ,					default=""      ,					help="Configuration file path or folder." )
										train_parser.add_argument(
										    "--finetuning_task_name"      ,					type=_lowerCamelCase      ,					default=_lowerCamelCase      ,					help="Optional fine-tuning task name if the TF model was a finetuned model."      ,					)
										train_parser.set_defaults(func=_lowerCamelCase )

				def __init__( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					*_lowerCamelCase      ,					):
										lowerCamelCase__   =logging.get_logger("transformers-cli/converting" )

										self._logger.info(F'''Loading model {model_type}''' )
										lowerCamelCase__   =model_type
										lowerCamelCase__   =tf_checkpoint
										lowerCamelCase__   =pytorch_dump_output
										lowerCamelCase__   =config
										lowerCamelCase__   =finetuning_task_name






				def 						_a      ( self ):
										if self._model_type == "albert":
																try:
																						from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
																						    convert_tf_checkpoint_to_pytorch,
																						)
																except ImportError:
																						raise ImportError(_lowerCamelCase )

																convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,					self._config      ,					self._pytorch_dump_output )
										elif self._model_type == "bert":
																try:
																						from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
																						    convert_tf_checkpoint_to_pytorch,
																						)
																except ImportError:
																						raise ImportError(_lowerCamelCase )

																convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,					self._config      ,					self._pytorch_dump_output )
										elif self._model_type == "funnel":
																try:
																						from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
																						    convert_tf_checkpoint_to_pytorch,
																						)
																except ImportError:
																						raise ImportError(_lowerCamelCase )

																convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,					self._config      ,					self._pytorch_dump_output )
										elif self._model_type == "t5":
																try:
																						from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
																except ImportError:
																						raise ImportError(_lowerCamelCase )

																convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,					self._config      ,					self._pytorch_dump_output )
										elif self._model_type == "gpt":
																from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
																    convert_openai_checkpoint_to_pytorch,
																)

																convert_openai_checkpoint_to_pytorch(self._tf_checkpoint      ,					self._config      ,					self._pytorch_dump_output )
										elif self._model_type == "transfo_xl":
																try:
																						from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
																						    convert_transfo_xl_checkpoint_to_pytorch,
																						)
																except ImportError:
																						raise ImportError(_lowerCamelCase )

																if "ckpt" in self._tf_checkpoint.lower():
																						lowerCamelCase__   =self._tf_checkpoint
																						lowerCamelCase__   =""
																else:
																						lowerCamelCase__   =self._tf_checkpoint
																						lowerCamelCase__   =""
																convert_transfo_xl_checkpoint_to_pytorch(
																    _lowerCamelCase      ,					self._config      ,					self._pytorch_dump_output      ,					_lowerCamelCase )
										elif self._model_type == "gpt2":
																try:
																						from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
																						    convert_gpta_checkpoint_to_pytorch,
																						)
																except ImportError:
																						raise ImportError(_lowerCamelCase )

																convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint      ,					self._config      ,					self._pytorch_dump_output )
										elif self._model_type == "xlnet":
																try:
																						from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
																						    convert_xlnet_checkpoint_to_pytorch,
																						)
																except ImportError:
																						raise ImportError(_lowerCamelCase )

																convert_xlnet_checkpoint_to_pytorch(
																    self._tf_checkpoint      ,					self._config      ,					self._pytorch_dump_output      ,					self._finetuning_task_name )
										elif self._model_type == "xlm":
																from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
																    convert_xlm_checkpoint_to_pytorch,
																)

																convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint      ,					self._pytorch_dump_output )
										elif self._model_type == "lxmert":
																from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
																    convert_lxmert_checkpoint_to_pytorch,
																)

																convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint      ,					self._pytorch_dump_output )
										elif self._model_type == "rembert":
																from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
																    convert_rembert_tf_checkpoint_to_pytorch,
																)

																convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,					self._config      ,					self._pytorch_dump_output )
										else:
																raise ValueError(
																    "--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]" )



"""simple docstring"""


# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a				={
    'configuration_mgp_str': ['MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MgpstrConfig'],
    'processing_mgp_str': ['MgpstrProcessor'],
    'tokenization_mgp_str': ['MgpstrTokenizer'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a				=[
	    'MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'MgpstrModel',
	    'MgpstrPreTrainedModel',
	    'MgpstrForSceneTextRecognition',
	]

if TYPE_CHECKING:
	from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
	from .processing_mgp_str import MgpstrProcessor
	from .tokenization_mgp_str import MgpstrTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mgp_str import (
		    MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MgpstrForSceneTextRecognition,
		    MgpstrModel,
		    MgpstrPreTrainedModel,
		)
else:
	import sys

	a				=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


